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Strategies

The three built-in selection strategies — momentum, mean reversion, smart beta — with their exact formulas, parameters, and default lookbacks.

A strategy ranks the universe at each rebalance; TradePilot then holds the top topN symbols and hands them to the optimizer for weighting. Three strategies ship built in (StrategyType = 'momentum' | 'meanReversion' | 'smartBeta'). You can try each one live on the strategy catalog.

Momentum

Selects assets with the strongest recent price move.

Momentum(i) = P(i, latest) − P(i, latest − t)

Symbols are ranked by descending momentum. Assets with fewer than t prices are skipped.

  • t — lookback in trading days (engine default 10).

Mean reversion

Selects assets trading furthest below their moving average (most oversold).

Deviation(i) = P(i, latest) − SMA(i, t)

Symbols are ranked by ascending deviation, so the most negative (most oversold) comes first. The default window is t = 20 trading days.

  • t / window — moving-average window in trading days (engine default 20).

Smart beta

Selects assets with the best risk-adjusted return — the ratio of mean return to its standard deviation over the window.

SmartBeta(i) = mean(R_i) / std(R_i)

Ranked by descending score (std uses the population form, ddof = 0; a zero std scores 0). Smart beta has no t; it uses the run’s window of returns.

Choosing parameters

  • topN caps how many ranked assets you actually hold — smaller = more concentrated.
  • t / window set the memory of the signal — shorter reacts faster and trades more; longer is smoother.
  • Turnover interacts with transaction costs: fast signals plus real costs can erase paper gains.

To search these systematically instead of guessing, use the Lab’s Sweep mode (t, topN, rebalanceFreq, and window are all sweepable).

Custom & AI strategies

Saved strategies store a type, a params object, and optional code — so the catalog also offers AI strategy templates alongside the three built-ins. See Strategies workspace.